Large language models (LLMs) are central to advancements in artificial intelligence, focusing on enhancing the models’ ability to follow detailed instructions. This area of research encompasses methods to improve the quality and complexity of datasets used for training LLMs, ultimately leading to more sophisticated and versatile AI systems. The importance of these improvements cannot be overstated, as they directly impact the models’ performance across various tasks, from natural language understanding to code generation and mathematical reasoning.
A major challenge in this field is the dependency on high-quality instruction datasets, which are difficult to annotate at scale. Manually designed methods require substantial human expertise and resources, making achieving consistent improvements across different tasks challenging. This limitation hinders the performance and adaptability of LLMs, creating a bottleneck in their development. Researchers have been actively exploring ways to overcome this bottleneck, seeking methods to enhance dataset complexity and diversity without requiring extensive human intervention.
Current methods, such as Evol-Instruct, iteratively refine high-quality data using LLMs to improve dataset complexity and diversity. While these methods are effective, they heavily rely on heuristic efforts and expert-designed evolving rules. This reliance can be expensive and time-consuming, particularly when adapting to new tasks. Evol-Instruct, for instance, has shown superior performance across various benchmarks, including MT-Bench, AlpacaEval, GSM8K, and HumanEval. However, each time it is applied to a new task, the methods for execution evolution need to be redesigned, requiring a high level of expertise and considerable costs.
Researchers from Microsoft introduced Auto Evol-Instruct, an automated framework that eliminates the need for human intervention in the instruction evolution process. This innovative approach leverages LLMs to design evolving methods autonomously, enabling cost-effective adaptation to various tasks by altering the input data. The framework begins with a universal initial evolving method that autonomously analyzes the input instructions and formulates evolution rules. These rules are then iteratively optimized by an optimizer LLM, which identifies and addresses issues in the evolving methods, ensuring minimal evolution failure and enhancing the dataset’s complexity and diversity.
Auto Evol-Instruct operates through a detailed process involving multiple stages. Firstly, it employs an initial evolving method that analyzes the input instruction and brainstorms evolution rules suitable for the given data. This method differs from Evol-Instruct, which requires human experts to specify the rules of evolution. Instead, Auto Evol-Instruct uses an evol LLM to devise a comprehensive plan based on the listed methods autonomously and implements this plan to generate the evolved instruction. The evol LLM then thoroughly reviews the evolved instruction, rectifying any unreasonable parts to ensure the final evolved instruction is complex and stable.
The performance of Auto Evol-Instruct was rigorously evaluated across several benchmarks. Using only 10K evolved ShareGPT data for fine-tuning Mixtral-8x7B, the framework achieved an impressive 8.09 on MT-Bench and 91.4 on AlpacaEval, surpassing GPT-3.5-Turbo and WizardLM-70B, and comparable with Claude2.0. Additionally, with just 7K evolved GSM8K training data, Auto Evol-Instruct achieved 82.49 on GSM8K, outperforming GPT-3.5-Turbo, WizardMath-70B, and MetaMath-70B. In code generation, using 20K evolved Code Alpaca to fine-tune DeepSeek-Coder-Base-33B, the framework achieved 77.4 on HumanEval, surpassing GPT-3.5-Turbo and WizardCoder-34B.
A key aspect of Auto Evol-Instruct is its ability to iteratively optimize the evolving method through Evol Trajectory Analysis and Evolving Method Optimization stages. The optimizer LLM analyzes the potential issues and failures exposed in instruction evolution performed by the evol LLM, generating feedback for subsequent optimization. This feedback is then used to refine the evolving method, ensuring the lowest failure rate for a given instruction dataset. This meticulous optimization and analysis ensures that the evolved datasets are complex and diverse, improving instruction tuning.
In conclusion, Auto Evol-Instruct addresses the limitations of manual methods by automating the evolution of instruction datasets. It provides a scalable, efficient solution that enhances the performance and adaptability of LLMs across various tasks. The research demonstrates that methods optimized by Auto Evol-Instruct significantly surpass those crafted by humans, showcasing its potential to advance the field of AI. The framework’s impressive results across multiple benchmarks highlight its effectiveness in improving instruction following, mathematical reasoning, and code generation capabilities.
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